Hourly Usage
Performance Metrics
Median Total Time
52.67s
Median TTFT
79.26s
Median Prefill TPS
84.45
Median Gen TPS
22.73
Model Information
Context Size
262144
Quantization
r64
Engine
vllm
Creation Method
LoRA Finetune
Model Type
Gemma31B
Chat Template
Gemma4
Reasoning
Yes
Vision
Yes
Parameters
31B
Added At
7/19/2026
license: apache-2.0 tags:
- merge
- mergekit
- roleplay model_creator: Ateron pipeline_tag: text-generation quantized_by: f16 language:
- en base_model:
- nbeerbower/Gemma4-Gutenberg-31B
- Gryphe/Pantheon-Reasoning-31B-1.1
- LatitudeGames/Equinox-31B
- MRockatansky/Gemma-4-31B-storymaxxed2
- ConicCat/Gemma4-GarnetV2-31B
🛡️ AssGuard
"Raise your shields, as the SLOP incoming"
⚔️ Overview
This is a thing I did for fun, experimenting here and there. But to my surprise this stuff can hit the mark and generate very funny outcomes. So I decided to let this model to live. Might steal your attention for several swipes.
Settings
I suggest to use high min_p from around 0.15-0.20. That's it all others is up to your experiments.
♻️ Mergekit Configuration
Below is the exact mergekit_config.yml recipe used to synthesize this model:
Phase 1: Asgard (dare_ties)
The place where the gods and warriors lives.
dare_ties Recipe 1
merge_method: dare_ties
base_model: F:\AI\Merge\Gemma-4-it
tokenizer_source: union
dtype: bfloat16
parameters:
lambda: 1.0
models:
- model: F:\AI\Merge\G4-Gutenberg
parameters:
density: [0.70, 0.70, 0.60, 0.60, 0.70]
weight:
- filter: mlp
value: [0.40, 0.45, 0.40, 0.40, 0.40]
- filter: self_attn
value: [0.30, 0.40, 0.50, 0.50, 0.50]
- value: [0.50, 0.50, 0.50, 0.50, 0.50]
- model: F:\AI\Merge\Pantheon-Reasoning-31B-1.1
parameters:
density: [0.30, 0.30, 0.40, 0.35, 0.25]
weight:
- filter: mlp
value: [0.40, 0.50, 0.40, 0.30, 0.10]
- filter: self_attn
value: [0.40, 0.35, 0.35, 0.30, 0.10]
- value: [0.30, 0.30, 0.40, 0.30, 0.10]
Phase 2: Odin (model_stock)
The headmaster himself!
model_stock Recipe 2
models:
- model: F:\AI\Merge\Equinox
- model: F:\AI\Merge\Gemma-4-31B-storymaxxed2
- model: F:\AI\Merge\GarnetV2
merge_method: model_stock
base_model: F:\AI\Merge\Gemma-4-it
dtype: bfloat16
tokenizer_source: union
Phase 3: AssGuard (dare_ties)
Preparation becomes!
dare_ties Recipe 3
merge_method: dare_ties
base_model: F:\AI\Merge\Gemma-4-it
tokenizer_source: union
dtype: bfloat16
parameters:
lambda: 1.0
models:
- model: F:\AI\Merge\Asgard
parameters:
density: [0.70, 0.70, 0.70, 0.70, 0.70]
weight:
- filter: mlp
value: [0.50, 0.50, 0.50, 0.50, 0.50]
- filter: self_attn
value: [0.40, 0.40, 0.40, 0.40, 0.40]
- value: [0.50, 0.50, 0.50, 0.50, 0.50]
- model: F:\AI\Merge\Odin
parameters:
density: [0.20, 0.30, 0.40, 0.30, 0.10]
weight:
- filter: mlp
value: [0.30, 0.40, 0.40, 0.40, 0.10]
- filter: self_attn
value: [0.30, 0.30, 0.30, 0.30, 0.10]
- value: [0.20, 0.30, 0.30, 0.20, 0.10]
🤝 Special Thanks
- Google DeepMind: For providing the base model.
- The Open-Source Community: Creators and all their fine-tuned models that were used in the merge.
- Mergekit Fork: Zerofata - For making mergekit work.
- To Nimbz: This cat for being a smart fella and assisting with advices.